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Findings on Data Mining Reported by Investigators at Hubei University of Technology (An Efficient Aggregation Scheme Resisting On Malicious Data Mining Attacks for Smart Grid)

Information Technology Daily

2020 JUL 08 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Investigators publish new report on Information Technology - Data Mining. According to news originating from Wuhan, People’s Republic of China, by NewsRx correspondents, research stated, “In the smart grid, efficient power supplies require near-real-time users’ electricity usage metering data, but these data might leak users’ private information, e.g., living habits. To address this problem, a number of privacy-preserving data aggregation schemes have been proposed in the literature.”

Financial supporters for this research include National Natural Science Foundation of China, Hubei Provincial Department of Education Key Project.

Our news journalists obtained a quote from the research from the Hubei University of Technology, “In this paper, we present a new type of attack, called malicious data mining attack, by which the adversary can infer a target user’s electricity usage data. When considering this attack, the majority of existing data aggregation schemes have one of the following two shortcomings. In one aspect, the schemes based on homomorphic encryption can output an accurate aggregation result, but most of them are vulnerable to this attack. In another aspect, the schemes based on differential privacy able to withstand this attack, but the random noises introduced prevent accurate aggregation results from being computed. In this paper, we propose a novel data aggregation scheme that is not only secure against the malicious data mining attack, but also capable of outputting an accurate aggregation result.”

According to the news editors, the research concluded: “Detailed security analyses indicate that the proposed scheme satisfies the desirable properties for privacy-preserving data aggregation in the smart grid, and the simulated results demonstrate that our proposed scheme enjoys low computation and communication overhead.”

For more information on this research see: An Efficient Aggregation Scheme Resisting On Malicious Data Mining Attacks for Smart Grid. Information Sciences, 2020;526():289-300. Information Sciences can be contacted at: Elsevier Science Inc, Ste 800, 230 Park Ave, New York, NY 10169, USA. (Elsevier -; Information Sciences -

The news correspondents report that additional information may be obtained from M.W. Zhang, Hubei University of Technology, School of Computer Science, Pob 430068, Wuhan 430068, People’s Republic of China. Additional authors for this research include H. Shen, Y.J. Liu and Z. Xia.

The direct object identifier (DOI) for that additional information is: This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.


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